仓库/Skill 库

使用先进的 RAG 系统发现你的下一部心仪动画，提供精准推荐与增强语义搜索。🎬 Discover your next favorite anime with this advanced Retrieval-Augmented Generation system, offering precise recommendations and enriched semantic search.

一个面向 PDF 文档问答的全栈 RAG 应用：上传 PDF，将其索引到本地向量库，然后基于页面级答案进行对话，并在内置阅读器中通过可点击引用跳转到对应页面。A full-stack retrieval-augmented generation (RAG) application for question answering over PDF documents. Upload a PDF, index it into a local vector store, then chat with page-grounded answers and clickable citations that jump to the right page in the built-in viewer.

基于 FastAPI + LangChain + RAG 的 AI 智能对话助手，支持多轮对话记忆、图片分析、流式回复、知识库 RAG 检索、上传定义知识库。

自托管、100% 本地的 AI 平台——在一个 Docker 栈中集成 LLM 推理、RAG 与知识图谱。无需 API key。Self-hosted, 100% local AI platform — LLM inference, RAG, and knowledge graphs in one Docker stack. No API keys.

基于 RAG 的文档问答机器人，可对任意 PDF/文本文件提问。使用本地 embeddings + Groq API。学习要点：embeddings、向量搜索、分块、RAG 流水线。Document Q&A bot using RAG (Retrieval-Augmented Generation). Ask questions about any PDF/text file. Uses local embeddings + Groq API. Learns: embeddings, vector search, chunking, RAG pipeline.

Rust 可嵌入的混合搜索原语：BM25、HNSW、reciprocal-rank fusion、UTF-8 安全的 chunking，零依赖。Embeddable hybrid search primitives for Rust: BM25, HNSW, reciprocal-rank fusion, UTF-8-safe chunking. Zero dependencies.

面向业务的 RAG | 要么有引用，要么不要 | 客服团队需要一份可核验的答案RAG for business | Citations or nothing | Support teams need an answer they can verify

🛠️ 使用 Haystack 轻松构建强大搜索系统，该框架用于开发端到端问答与搜索应用。🛠️ Build powerful search systems effortlessly with Haystack, a framework for developing end-to-end question answering and search applications.

RAG 真的物有所值吗？ragornot 在真实 AWS Lambda + Bedrock 后端上，通过四种检索模式（Flat/BM25、Hierarchical、LLM-only、RAG）运行相同查询，并测量延迟、质量、成本和碳排放——用数据帮你决定是否使用 RAG。静态 Next.js 部署于 GitHub Pages。Does RAG actually earn its cost? ragornot runs the same query through four retrieval modes (Flat/BM25, Hierarchical, LLM-only, RAG) against a live AWS Lambda + Bedrock backend and measures latency, quality, cost, and carbon — so you can decide RAG-or-not with data. Static Next.js on GitHub Pages.

在同一 chunk 集合上对比 Lexical / Vector / Graph RAG，提供确定性评估、自我修正的 LangGraph agent 循环、PII 治理与 RAG 就绪度分析器。Lexical vs Vector vs Graph RAG over one identical chunk set, with deterministic evaluation, a self-correcting LangGraph agent loop, PII governance, and a RAG-readiness analyzer.

📚 构建并评估 RAG 流水线，实现数据导入、嵌入、检索与问答，并提供准确性与相关性指标。📚 Build and evaluate RAG pipelines to ingest, embed, retrieve, and answer questions with metrics for accuracy and relevance.

使用本地 LLM 与私密法律文档对话，在自有硬件上获得带引用、可验证的答案。Chat with private legal documents using local LLMs. Get cited, verifiable answers on your own hardware.

为 MI Tech Arsenal 定制的基于 RAG 的 AI 助手，具备自动化 sitemap 索引、通过 ChromaDB 进行神经搜索，以及 Streamlit 到 Blogger 的无缝集成。A custom RAG-based AI assistant for MI Tech Arsenal. Features automated sitemap indexing, neural search via ChromaDB, and a seamless Streamlit-to-Blogger integration.

🛠️ 通过 ACG 增强 RAG 系统，依托可靠的外部知识提升准确性与事实一致性，减少 LLM 响应中的幻觉。🛠️ Enhance RAG systems with ACG to reduce hallucinations in LLM responses by improving accuracy and grounding in reliable external knowledge.

使用本地 RAG 系统增强知识库，借助混合搜索实现精准信息检索与最优结果🔍 Enhance your knowledge base with a local RAG system that leverages hybrid search for precise information retrieval and optimal results.

为 Ramone 提供的容器化本地 RAG 服务，使用 atlas-corpus 检索、ChromaDB 会话记忆和 Ollama 生成。Containerised local RAG service for Ramone using atlas-corpus retrieval, ChromaDB session memory and Ollama generation.

数据在被构建成有意义的东西之前只是噪声——将原始数据转化为真正可用的系统，涵盖欺诈检测、RAG pipeline、计算机视觉追踪器以及数据仓库等领域。FAST NUCES 数据科学本科生，在"搞坏东西"和"交付产品"之间反复横跳。持续构建，持续学习，欢迎合作和实习交流Data is noise until someone builds something meaningful out of it, turn raw data into systems that actually work, from fraud detection and rag pipelines to computer vision trackers and data warehouses. DS undergrad at FAST NUCES, somewhere between breaking things and shipping them. always building, always learning, open to collabs and interns

这是一个个人日志/日记/知识库，集成本地部署的聊天机器人，统一追踪我想要追踪的任何内容。This is a personal journal / diary / knowledge base integrating a locally served chatbot to cohesively track whatever I want it to track.

基于 FastAPI、LangChain、ChromaDB 和 Google Gemini API 构建的快速、轻量级 RAG 聊天机器人。A fast, lightweight Retrieval-Augmented Generation (RAG) chatbot built with FastAPI, LangChain, ChromaDB, and the Google Gemini API.

AI 会议智能工作区，支持视频转录、结构化洞察以及基于转录的 RAG 对话。AI meeting intelligence workspace for video transcription, structured insights, and transcript-grounded RAG conversations.

学术知识图谱系统——符号驱动的研究发现，配合轻量级向量检索。Academic knowledge graph system — symbol-driven research discovery with lightweight vector retrieval

可移植契约与 local-first 运行时，覆盖规范数据、检索、持久执行及面向 agent 的 AI。Portable contracts and local-first runtimes for canonical data, retrieval, durable execution, and agent-facing AI.

校园社区：地理位置范围内容 · RAG 混合检索 · 问题追踪 · AI 助手（千问 v3-flash）

以挑战为中心的多跳 RAG 研究枢纽：五类挑战分类法、208 篇文献综述快照，以及七篇综述参考库。Research hub for challenge-centered multi-hop RAG: five-challenge taxonomy, 208-work review snapshot, and seven-survey reference library.

Chrome 扩展，将 RAG 驱动的 AI 辅导带入任何学习管理系统（LMS），从高中到大学。BYOK Gemini API + Backboard.io 后端。为 CUTC Transform Hackathon 2026 构建。Chrome extension that brings RAG-powered AI tutoring to any Learning Management System (LMS); from high school to university. BYOK Gemini API + Backboard.io backend. Built for CUTC Transform Hackathon 2026.

基于 LGPD、CDC 与 CLT 的 RAG：使用标准答案衡量检索效果，对比 BM25、embeddings 与混合方案，并通过一次使最常见 guard-rail 失败的弃答测试RAG sobre LGPD, CDC e CLT — recuperação medida com gabarito, comparação de BM25, embeddings e híbrido, e um teste de abstenção que reprovou o guard-rail mais comum

無料のローカルAIインストーラ。ダブルクリック一発・初期設定なしで、チャット・RAG（文書質問）・Web検索が使えるWindows向けローカルAI環境（Ollama + Open WebUI + qwen3.5）

AI-Native 钢琴学习 RAG 助手——在线且免费（GitHub Pages -> Google Cloud Run -> Neon pgvector -> Groq）：具备引用、护栏与内置可观测性（延迟/token/成本）的摄取与查询流水线（rewrite -> 混合检索/RRF -> rerank -> LLM），数据持久化至可检索数据库。Node.js 实现，不依赖付费 API。AI-Native Piano Learning RAG Assistant - live & free (GitHub Pages -> Google Cloud Run -> Neon pgvector -> Groq): ingestion + query pipeline (rewrite -> hybrid search/RRF -> rerank -> LLM) with citations, guardrails, and built-in observability (latency/tokens/cost) persisted to a searchable DB. Node.js, no paid APIs.

AI 驱动的 DevSecOps 可观测平台，基于 Gemini 2.5、LangChain 与 RAG 驱动的 runbook 监控基础设施、检测威胁并自动响应。AI-powered DevSecOps observability platform; monitors infrastructure, detects threats, and responds using Gemini 2.5, LangChain, and RAG-powered runbooks.

端到端 Python RAG 框架，包含文档摄取、语义搜索、对话式 AI、多用户检索、可观测性、结构化输出与评估。An end-to-end Python RAG framework featuring document ingestion, semantic search, conversational AI, multi-user retrieval, observability, structured outputs, and evaluation.

使用 OpenAI Python SDK 嵌入开发者工具文档并对查询匹配进行排序。Embed developer-tool documents and rank query matches with the OpenAI Python SDK.

CampusIQ 是由 AI 驱动的学术知识管理与问答系统，借助 RAG、语义搜索、OCR、图像理解以及基于 LLM 的回答生成，实现教育内容的智能检索与分析。CampusIQ is an AI-powered academic knowledge management and question-answering system that enables intelligent retrieval and analysis of educational content using RAG, semantic search, OCR, image understanding, and LLM-based response generation.

🧠 为 Telegram、Discord 与 Reddit 打造的私有、源码溯源知识库，基于混合 RAG，并提供每周摘要。🧠 Private, source-grounded knowledge base for Telegram, Discord, and Reddit with hybrid RAG and weekly digests.

基于已验证决策构建的共享 intelligence 层。A shared intelligence layer built from verified decisions.

混合 RAG 系统，具备 reranking、基于引用的 grounded citations，以及可选 provider-backed 执行的可确定离线评估分级。A hybrid RAG system with reranking, grounded citations, and deterministic offline evaluation tiers with optional provider-backed execution.